This paper analyses the computational issues of full 3-D tomography, in which the starting model as well as the model perturbation is 3-D and the sensitivity (Frechet) kernels are calculated using the full physics of 3-D wave propagation. We compare two formulations of the structural inverse problem: the adjoint-wavefield (AW) method, which back-propagates the data from the receivers to image structure, and the scattering-integral (SI) method, which sets up the inverse problem by calculating and storing the Frechet kernels for each data functional. The two inverse methods are closely related, but which one is more efficient depends on the overall problem geometry, particularly on the ratio of sources to receivers, as well as trade-offs in computational resources, such as the relative costs of compute cycles to data storage. We find that the SI method is computationally more efficient than the AW method in regional waveform tomography using large sets of natural sources, although it requires more storage.
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